Abstract:
Various applications like face analysis, recognition, reidentification exist where the use
of Face Detection is necessary as their preprocessing algorithm in the pipeline. There
have been extensive studies done in the domain of Face Detection in the past, and
various robust algorithms have been proposed and evaluated on different datasets. Such
techniques are also deployed in various applications. Although it may seem that this
domain is very old and much work must have been done in it, there is still room for
improvement. Previous studies have targeted issues like facial poses, expressions, scales
of images and occlusions, and have achieved good accuracy. In recent years, work on
advanced issues like low-resolution images, usage of proposed anchors, scale-invariance
of models, minimization of model size, have been explored and various solutions have
been proposed.
The proposed research work intends to experiment and evaluate various object detection
techniques, designed specifically for frontal faces, on a dataset of images containing
faces. The dataset is prepared using the images from a live feed of a news channel
and contains various illumination, scale, quality variations making the dataset complex
enough to test the techniques on. Another dataset of face images containing medical face
masks is used, to evaluate how good well such models perform in presence of occlusions
like surgical masks. These models tested on these datasets are evaluated based on
the Mean Average Precision (mAP) metric which uses Intersection over Union (IoU)
to calculate the True Positive, False Positive, True Negative, and False Negative for
accurate precision calculation. Results are compiled and compared to finalize which
type of model works best for the datasets under observation. .
